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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big DEPARTMENTAL SEMINAR}
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\centerline{4:15 p.m., Tuesday, November 9, 2004}
\centerline{Sequoia Hall Room 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

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\centerline{\sl Robert J. Tibshirani}
\centerline{\sl Stanford University}
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\centerline{\bf Prediction by Supervised Principal Components}
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Abstract: In regression problems where the number of predictors
greatly
  exceeds the number of observations, conventional regression
  techniques may produce unsatisfactory results.  We describe a
  technique called supervised principal components that can be applied
  to this type of problem.  Supervised principal components is similar
  to conventional principal components analysis except that it uses a
  subset of the predictors that are selected based on their
  association with the outcome.  Supervised principal components
  can be applied to regression and generalized regression problems
  such as survival analysis.  It compares favorably to other
  techniques for this type of problem, and can also account for the
  effects of other covariates and help identify which predictor
  variables are most important. We also provide asymptotic consistency 
results to help support our empirical findings. These methods could become important
  tools for DNA microarray data, where they may be used to more
  accurately diagnose and treat cancer.

This is joint work with Eric Bair, Trevor Hastie and Debashis Paul.
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